Agentic AI · Mumbai, India

AI agents that do the work — safely.

An AI agent is software that can plan a multi-step task, use your tools and data, and complete it with minimal supervision. I help businesses in India design, build and govern agents that work inside real systems — with permissions, approvals and audit trails from day one.

Discuss an agent use case See examples

In shortAI agents combine a large language model with tools (APIs, databases, email, browsers), memory and rules. Unlike a chatbot, an agent can take actions — create a ticket, update a CRM record, draft and send a report. Around 40% of Indian organisations already use agentic AI and close to 50% more plan to adopt it within a year (IDC–UiPath, 2025), but skills, data security and transparency remain the top concerns — which is exactly what a well-governed design addresses.

Examples

Agents worth building in 2026

Sales research agent

Researches a prospect, summarises fit, drafts a tailored email and logs everything in the CRM for a salesperson to approve.

Customer service agent

Resolves order, billing and service queries across web and WhatsApp by checking your systems — and escalates edge cases to humans.

Finance operations agent

Matches invoices with purchase orders and receipts, flags mismatches and prepares entries for review.

Intranet knowledge agent

Answers employee questions from policies and SOPs, raises IT/HR tickets and follows up until closed.

Website performance agent

Monitors Core Web Vitals, detects regressions after releases and opens a ticket with the likely cause.

Management reporting agent

Pulls numbers from ERP and spreadsheets, writes the weekly commentary and circulates it to leadership.

Guardrails

How I keep agents safe and accountable

Least-privilege access — agents only see and do what their role allows.
Human approval for payments, deletions and external communication.
Full audit trail of every step, tool call and decision.
Evaluation & testing on real scenarios before go-live, monitoring after.
Prompt-injection and data-leak protection, with private models where needed.

Readiness checklist

Is your organisation agent-ready?

Your key systems (CRM, ERP, ticketing) have APIs or integration options.
The process is documented well enough to explain to a new employee.
There is a clear owner who will review the agent’s work.
You can measure today’s time, cost or error rate for the task.

Ticked three or more? You’re ready for a pilot.

FAQ

AI agents — common questions

What is the difference between an AI agent and a chatbot?

A chatbot answers questions. An AI agent can also take actions: it plans a sequence of steps, uses tools such as your CRM, ERP, email or database, checks the results and completes a task, asking a human for approval where required.

Are AI agents reliable enough for business use?

They are reliable for well-defined tasks when designed with narrow permissions, validation steps, human approval for high-impact actions and continuous monitoring. Starting with one process and expanding gradually is the safest path.

Which platforms are used to build AI agents?

Agents can be built with model providers’ agent frameworks (OpenAI, Anthropic, Google, Microsoft), open-source orchestration libraries, or workflow tools such as n8n — selected based on your systems, security needs and budget.

How do we measure ROI from an AI agent?

Measure the baseline first — time per task, cost, error rate and turnaround — then compare after deployment. Good agent projects define these metrics before a single line of code is written.

Keep exploring

Agent pilot

Pick one process. We’ll build the agent around it.

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